Anuradha Durairaj, Subramani Neelakandan, Khalaf Osamah Ibrahim, Alotaibi Youseef, Alghamdi Saleh, Rajagopal Manjula
Department of Computer Science and Business Systems, Panimalar Engineering College, Chennai 600123, India.
Department of Computer Science and Engineering, R.M.K Engineering College, Chennai 600123, India.
Sensors (Basel). 2022 Apr 8;22(8):2867. doi: 10.3390/s22082867.
Underwater wireless sensor networks (UWSNs) have applications in several fields, such as disaster management, underwater navigation, and environment monitoring. Since the nodes in UWSNs are restricted to inbuilt batteries, the effective utilization of available energy becomes essential. Clustering and routing approaches can be employed as energy-efficient solutions for UWSNs. However, the cluster-based routing techniques developed for conventional wireless networks cannot be employed for a UWSN because of the low bandwidth, spread stay, underwater current, and error probability. To resolve these issues, this article introduces a novel chaotic search-and-rescue-optimization-based multi-hop data transmission (CSRO-MHDT) protocol for UWSNs. When using the CSRO-MHDT technique, cluster headers (s) are selected and clusters are prearranged, rendering a range of features, including remaining energy, intracluster distance, and intercluster detachment. Additionally, the chaotic search and rescue optimization (CSRO) algorithm is discussed, which is created by incorporating chaotic notions into the classic search and rescue optimization (SRO) algorithm. In addition, the CSRO-MHDT approach calculates a fitness function that takes residual energy, distance, and node degree into account, among other factors. A distinctive aspect of the paper is demonstrated by the development of the CSRO algorithm for route optimization, which was developed in-house. To validate the success of the CSRO-MHDT method, a sequence of tests were carried out, and the results showed the CSRO-MHDT method to have a packet delivery ratio (PDR) of 88%, whereas the energy-efficient clustering routing protocol (EECRP), the fuzzy C-means and moth-flame optimization (FCMMFO), the fuzzy scheme and particle swarm optimization (FBCPSO), the energy-efficient grid routing based on 3D cubes (EGRC), and the low-energy adaptive clustering hierarchy based on expected residual energy (LEACH-ERE) methods have reached lesser PDRs of 83%, 81%, 78%, 77%, and 75%, respectively, for 1000 rounds. The CSRO-MHDT technique resulted in higher values of number of packets received (NPR) under all rounds. For instance, with 50 rounds, the CSRO-MHDT technique attained a higher NPR of 3792%.
水下无线传感器网络（UWSN）在多个领域都有应用，如灾害管理、水下导航和环境监测。由于UWSN中的节点依赖内置电池，有效利用可用能量变得至关重要。聚类和路由方法可作为UWSN的节能解决方案。然而，由于低带宽、传播时延、水下水流和错误概率等因素，为传统无线网络开发的基于簇的路由技术无法用于UWSN。为了解决这些问题，本文为UWSN引入了一种基于新颖的混沌搜索与救援优化的多跳数据传输（CSRO-MHDT）协议。在使用CSRO-MHDT技术时，会选择簇头并预先安排簇，呈现出一系列特征，包括剩余能量、簇内距离和簇间距离。此外，还讨论了混沌搜索与救援优化（CSRO）算法，该算法是通过将混沌概念融入经典搜索与救援优化（SRO）算法而创建的。此外，CSRO-MHDT方法计算一个适应度函数，该函数除其他因素外，还考虑剩余能量、距离和节点度。本文的一个独特之处在于内部开发的用于路由优化的CSRO算法。为了验证CSRO-MHDT方法的成功，进行了一系列测试，结果表明CSRO-MHDT方法的数据包交付率（PDR）为88%，而节能聚类路由协议（EECRP）、模糊C均值和蛾火优化（FCMMFO）、模糊方案和粒子群优化（FBCPSO）、基于3D立方体的节能网格路由（EGRC）以及基于预期剩余能量的低能量自适应聚类层次结构（LEACH-ERE）方法在1000轮测试中分别达到了较低的PDR，为83%、81%、78%、77%和75%。在所有轮次中，CSRO-MHDT技术的接收数据包数量（NPR）值更高。例如，在50轮测试中，CSRO-MHDT技术获得了更高的NPR，为3792%。
